Forecasting Methods Questions And Answers

Choose one of the forecasting methods and explain the rationale behind using it in real-life.

Judgment and opinion forecast method relies on evaluation of subjective inputs attained from a number of sources such as panels of experts, executives and managers, the sale staff, and consumer survey. The above sources normally give insights that are not available in the given situation and thus, giving an opportunity to make wise judgment or opinion based on what took place in similar past events.

Describe how a domestic fast food chain with plans for expanding into China would be able to use a forecasting model “Forecasting Models”.

A fast food chain intending to expand into China would first need to collect available data and information regarding food chain business in China. The company will then use one of the accessible forecasting models to evaluate the data and establish if they are going to be successful in China.

Please respond to the following:

What is the difference between a causal model and a time- series model? Give example of when each would be used.

Time series forecasting method is a qualitative approach that evaluates data collected over a period of time to identify trends. On the other hand, causal method employs a single previous value of time series as the foundation of a forecast, and it can be employed with a trend, seasonal variations, or stable series. Their major difference is that time series tries various methods and selects the most effective one, while Casual method employs non-linear or linear and multiple or singular regression analysis, to establish the association that lowers mean squared forecasting error. Time series is employed for short term forecasting while casual is employed for long term forecasting (n.a., 2001).

What are some of the problems and drawbacks of the moving average forecasting model?

Moving average forecast model is founded on an artificially generated time series wherein the mean for a provided time period is substituted by that value mean and the values for certain number of succeeding and preceding time periods. The disadvantages of using this model are that: It does not give an actual equation, and thus it is not very helpful as a medium-long range forecasting tool. The model can only be reliably employed to forecast two or one period in future. Moving average tracks actual data, however, it always lags behind it. In addition, it will never reach the valleys or peaks of the real data, it smoothes out the data instead (n.a., 2001).

How do you determine how many observations to average in a moving average model? 

Moving average forecast traces the definite data with one period lag. It employs a number of the most recent definite data values in making a forecast. The number of observations to be averaged in a moving average model is determined by the period value.

How do you determine the weightings to use in a weighted moving average model?

The choice of weights in weighted moving average is somewhat arbitrary and usually involves the employment of trial and error to establish appropriate weighting scheme.

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